Hi guys I cant even compile my code because i have so many errors. Could someone please take a look at it. Thanks so much!!!
import pandas as pd
import numpy as np
import datetime
import math
import talib
from datetime import timedelta
#### Variables to change for your own liking #################
def initialize(context):
#### Variables to change for your own liking #################
#the constant for portfolio turnover rate
#number of stocks to pass through the fundamental screener
context.num_screener_start = 500
context.num_screener_end = 50
#number of stocks in portfolio at any time
context.num_stock = 20
#################################################################
#month counter for holding period logic.
context.holding_months = 12
#context.month_count = context.holding_months
context.month_count = context.holding_months
context.target_leverage = 0.99
context.hedge_symbol = sid(21519) #IWM
context.hedge_ratio = 0.40
schedule_function(rebalance, date_rules.month_end(7), time_rules.market_close(hours=2, minutes=30))
def before_trading_start(context,data):
"""
Called before the start of each trading day.
It updates our universe with the
securities and values found from fetch_fundamentals.
"""
#this code prevents query every day
if context.month_count != context.holding_months:
return
fundamental_df = get_fundamentals(
query(
# put your query in here by typing "fundamentals."
fundamentals.valuation_ratios.ev_to_ebitda,
fundamentals.valuation_ratios.pb_ratio,
fundamentals.valuation_ratios.ps_ratio,
fundamentals.valuation_ratios.pcf_ratio,
fundamentals.valuation_ratios.pe_ratio,
fundamentals.valuation_ratios.total_yield,
fundamentals.valuation_ratios.book_value_yield,
fundamentals.earnings_ratios.diluted_cont_eps_growth,
fundamentals.earnings_ratios.diluted_eps_growth,
fundamentals.earnings_ratios.dps_growth,
fundamentals.earnings_report.diluted_eps,
fundamentals.operation_ratios.roe,
fundamentals.valuation_ratios.payout_ratio,
fundamentals.valuation_ratios.sustainable_growth_rate
)
.filter(fundamentals.valuation.market_cap > 10e6)
.filter(fundamentals.income_statement.ebit > 0)
.filter(fundamentals.valuation_ratios.ev_to_ebitda > 0)
.filter(fundamentals.valuation.shares_outstanding != None)
.filter(fundamentals.valuation.market_cap != None)
.filter(fundamentals.earnings_report.diluted_eps>0)
.order_by(fundamentals.valuation_ratios.ev_to_ebitda.asc())
.limit(context.num_screener_end)
)
for stock in fundamental_df:
bookValue = fundamental_df[stock]['book_value_yield']
discRate = 0.05
eps1 = fundamental_df[stock]['diluted_eps']
eps2 = fundamental_df[stock]['diluted_eps']*fundamental_df[stock]['diluted_eps_growth']
epsGrowth = fundamental_df[stock]['diluted_cont_eps_growth']
divPayout = fundamental_df[stock]['payout_ratio']
avgROE = fundamental_df[stock]['roe']
bookValueOrig = bookValue # original book value
#Year 1
roe = float(eps1)/bookValue #find return on equity year 1
pbcum = 1 +(roe - discRate) / (1+ discRate) #find the pbcum for year 1
cg =1 #cumulated growth
#Year 2
bookValue = bookValue + (eps1 * (1 - divPayout)) #find book value year 2
cg = cg * (1 + (1 - divPayout) * roe) #find cumulated growth year 2
roe = eps2 / bookValue #find the ROE in year 2
pbcum = ((roe - discRate) * cg)/ (1 + discRate)**2 + pbcum#find the pbcum in year 2
feps = eps2 #assign
for x in range(3, 8): #for loop from year 3 to 8
bookValue = (bookValue + feps * (1 - divPayout)) #assign new book value
feps = feps * (1 + epsGrowth); #new eps
cg = cg * (1 + (1 - divPayout) * roe) #new cumulated growth
roe = feps / bookValue; #new return on equity
pbcum = (roe - discRate) * cg / (1 + discRate)** x + pbcum #new pbcum
ROEY8 = roe #assign new ROE for years 9 onwards
for x in range(8, 16): #for loop from year 9 to 16
cg = cg * (1 + (1 - divPayout) * roe) #new cumulated growth
roe = roe - ((ROEY8 - avgROE) / 8) #new return on equity
pbcum = (roe - discRate) * cg / (1 + discRate)**x + pbcum #new pbcum
#find morningstar value
context.aspectmodelnew = bookValueOrig * (pbcum + ((roe - discRate) * cg) /(discRate * ((1 + discRate)**15)))
pass
#Pull current data
#checks if the stocks data is higher or lower, depending on which it will short or long
# Filter out only stocks that fits in criteria
context.stocks = [stock for stock in fundamental_df]
# Update context.fundamental_df with the securities that we need
context.fundamental_df = fundamental_df[context.stocks]
update_universe(context.fundamental_df.columns.values)
def ranking(context,data):
pricelist = {}
for stock in context.fundamental_df:
Current = data[stock].price
# newPrice = Current - context.aspectmodelnew
perc = (context.aspectmodelnew-Current)/Current
pricelist.update({stock,perc})
#pricelist[stock] = perc
sorted(pricelist)
print "%.2f" % pricelist
print "%.2f" %perc
context.upper_percentile = np.percentile(perc, 80)
pass
#print(perc)
#pricelist.update({context.stock,perc})
#pricelist.update(map(stock,perc))
#sorted(pricelist)
#buy = pricelist.itervalues().next()
#buy = pricelist.values.itervalues()
#print buy
for stock,perc in pricelist.items():
print(stock)
print(perc)
pricelist.clear()
# newPrice = 0
def rebalance(context, data):
# track how many positions we're holding
#pricelist = {}
if context.month_count >= context.holding_months:
context.month_count = 1
else:
context.month_count += 1
return
# Exit all positions before starting new ones
for stock in context.portfolio.positions:
if stock not in context.fundamental_df:
order_target_percent(stock, 0)
# Create weights for each stock
weight = create_weights(context, context.stocks)
# Rebalance all stocks to target weights
for stock in context.fundamental_df:
if weight != 0 and stock in data:
Current = data[stock].price
#newlist = sorted(pricelist, key=itemgetter(newPrice))
if Current < context.aspectmodelnew*0.8 :
print(context.buy)
print "%.2f" %context.pricelist
print(stock)
order_target_percent(context.upper_percentile,weight) # Long
#elif Current > context.aspectmodelnew*1.2:
# order_target_percent(stock, -weight) # Short
else:
order_target_percent(stock, 0)
#order_target_percent(stock, weight)
record(leverage=context.account.leverage)
#def calculate(context, data):
def handle_data (context,data):
bookValue = fundamental_df[stock]['book_value_yield']
discRate = 0.05
eps1 = fundamental_df[stock]['diluted_eps']
eps2 = fundamental_df[stock]['diluted_eps']*fundamental_df[stock] ['diluted_eps_growth']
epsGrowth = fundamental_df[stock]['diluted_cont_eps_growth']
divpayout = fundamental_df[stock]['payout_ratio']
avgROE = fundamental_df[stock]['roe']
bookValueOrig = bookValue # original book value
#Year 1
roe = float(eps1)/bookValue #find return on equity year 1
pbcum = 1 +(roe - discRate) / (1+ discRate) #find the pbcum for year 1
cg =1 #cumulated growth
#Year 2
bookValue = bookValue + (eps1 * (1 - divPayout)) #find book value year 2
cg = cg * (1 + (1 - divPayout) * roe) #find cumulated growth year 2
roe = eps2 / bookValue #find the ROE in year 2
pbcum = ((roe - discRate) * cg)/ (1 + discRate)**2 + pbcum#find the pbcum in year 2
feps = eps2 #assign
for x in range(3, 8): #for loop from year 3 to 8
bookValue = (bookValue + feps * (1 - divPayout)) #assign new book value
feps = feps * (1 + epsGrowth); #new eps
cg = cg * (1 + (1 - divPayout) * roe) #new cumulated growth
roe = feps / bookValue; #new return on equity
pbcum = (roe - discRate) * cg / (1 + discRate)** x + pbcum #new pbcum
ROEY8 = roe #assign new ROE for years 9 onwards
for x in range(8, 16): #for loop from year 9 to 16
cg = cg * (1 + (1 - divPayout) * roe) #new cumulated growth
roe = roe - ((ROEY8 - avgRoe) / 8) #new return on equity
pbcum = (roe - discRate) * cg / (1 + discRate)**x + pbcum #new pbcum
#find morningstar value
aspectModelNew = bookValueOrig * (pbcum + ((roe - discRate) * cg) /(discRate * ((1 + discRate)**15)))
#Pull current data
Current = data[context.stock].price
#checks if the stocks data is higher or lower, depending on which it will short or long
def create_weights(context, stock):
"""
Takes in a list of securities and weights them all equally
"""
if len(stock) == 0:
return 0
else:
weight = 0.99/len(stock)
return weight
record(leverage=context.account.leverage) 
